"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import math
from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F


from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .layers import SelectAdaptivePool2d, Linear, make_divisible
from .efficientnet_blocks import SqueezeExcite, ConvBnAct
from .helpers import build_model_with_cfg
from .registry import register_model


__all__ = ['GhostNet']


def _cfg(url='', **kwargs):
    return {
        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (1, 1),
        'crop_pct': 0.875, 'interpolation': 'bilinear',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'conv_stem', 'classifier': 'classifier',
        **kwargs
    }


default_cfgs = {
    'ghostnet_050': _cfg(url=''),
    'ghostnet_100': _cfg(
        url='https://github.com/huawei-noah/CV-backbones/releases/download/ghostnet_pth/ghostnet_1x.pth'),
    'ghostnet_130': _cfg(url=''),
}


_SE_LAYER = partial(SqueezeExcite, gate_layer='hard_sigmoid', rd_round_fn=partial(make_divisible, divisor=4))


class GhostModule(nn.Module):
    def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True):
        super(GhostModule, self).__init__()
        self.oup = oup
        init_channels = math.ceil(oup / ratio)
        new_channels = init_channels * (ratio - 1)

        self.primary_conv = nn.Sequential(
            nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False),
            nn.BatchNorm2d(init_channels),
            nn.ReLU(inplace=True) if relu else nn.Sequential(),
        )

        self.cheap_operation = nn.Sequential(
            nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False),
            nn.BatchNorm2d(new_channels),
            nn.ReLU(inplace=True) if relu else nn.Sequential(),
        )

    def forward(self, x):
        x1 = self.primary_conv(x)
        x2 = self.cheap_operation(x1)
        out = torch.cat([x1, x2], dim=1)
        return out[:, :self.oup, :, :]


class GhostBottleneck(nn.Module):
    """ Ghost bottleneck w/ optional SE"""

    def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3,
                 stride=1, act_layer=nn.ReLU, se_ratio=0.):
        super(GhostBottleneck, self).__init__()
        has_se = se_ratio is not None and se_ratio > 0.
        self.stride = stride

        # Point-wise expansion
        self.ghost1 = GhostModule(in_chs, mid_chs, relu=True)

        # Depth-wise convolution
        if self.stride > 1:
            self.conv_dw = nn.Conv2d(
                mid_chs, mid_chs, dw_kernel_size, stride=stride,
                padding=(dw_kernel_size-1)//2, groups=mid_chs, bias=False)
            self.bn_dw = nn.BatchNorm2d(mid_chs)
        else:
            self.conv_dw = None
            self.bn_dw = None

        # Squeeze-and-excitation
        self.se = _SE_LAYER(mid_chs, rd_ratio=se_ratio) if has_se else None

        # Point-wise linear projection
        self.ghost2 = GhostModule(mid_chs, out_chs, relu=False)
        
        # shortcut
        if in_chs == out_chs and self.stride == 1:
            self.shortcut = nn.Sequential()
        else:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    in_chs, in_chs, dw_kernel_size, stride=stride,
                    padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False),
                nn.BatchNorm2d(in_chs),
                nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(out_chs),
            )

    def forward(self, x):
        shortcut = x

        # 1st ghost bottleneck
        x = self.ghost1(x)

        # Depth-wise convolution
        if self.conv_dw is not None:
            x = self.conv_dw(x)
            x = self.bn_dw(x)

        # Squeeze-and-excitation
        if self.se is not None:
            x = self.se(x)

        # 2nd ghost bottleneck
        x = self.ghost2(x)
        
        x += self.shortcut(shortcut)
        return x


class GhostNet(nn.Module):
    def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2, in_chans=3, output_stride=32, global_pool='avg'):
        super(GhostNet, self).__init__()
        # setting of inverted residual blocks
        assert output_stride == 32, 'only output_stride==32 is valid, dilation not supported'
        self.cfgs = cfgs
        self.num_classes = num_classes
        self.dropout = dropout
        self.feature_info = []

        # building first layer
        stem_chs = make_divisible(16 * width, 4)
        self.conv_stem = nn.Conv2d(in_chans, stem_chs, 3, 2, 1, bias=False)
        self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module=f'conv_stem'))
        self.bn1 = nn.BatchNorm2d(stem_chs)
        self.act1 = nn.ReLU(inplace=True)
        prev_chs = stem_chs

        # building inverted residual blocks
        stages = nn.ModuleList([])
        block = GhostBottleneck
        stage_idx = 0
        net_stride = 2
        for cfg in self.cfgs:
            layers = []
            s = 1
            for k, exp_size, c, se_ratio, s in cfg:
                out_chs = make_divisible(c * width, 4)
                mid_chs = make_divisible(exp_size * width, 4)
                layers.append(block(prev_chs, mid_chs, out_chs, k, s, se_ratio=se_ratio))
                prev_chs = out_chs
            if s > 1:
                net_stride *= 2
                self.feature_info.append(dict(
                    num_chs=prev_chs, reduction=net_stride, module=f'blocks.{stage_idx}'))
            stages.append(nn.Sequential(*layers))
            stage_idx += 1

        out_chs = make_divisible(exp_size * width, 4)
        stages.append(nn.Sequential(ConvBnAct(prev_chs, out_chs, 1)))
        self.pool_dim = prev_chs = out_chs
        
        self.blocks = nn.Sequential(*stages)        

        # building last several layers
        self.num_features = out_chs = 1280
        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        self.conv_head = nn.Conv2d(prev_chs, out_chs, 1, 1, 0, bias=True)
        self.act2 = nn.ReLU(inplace=True)
        self.flatten = nn.Flatten(1) if global_pool else nn.Identity()  # don't flatten if pooling disabled
        self.classifier = Linear(out_chs, num_classes) if num_classes > 0 else nn.Identity()

    def get_classifier(self):
        return self.classifier

    def reset_classifier(self, num_classes, global_pool='avg'):
        self.num_classes = num_classes
        # cannot meaningfully change pooling of efficient head after creation
        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        self.flatten = nn.Flatten(1) if global_pool else nn.Identity()  # don't flatten if pooling disabled
        self.classifier = Linear(self.pool_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        x = self.conv_stem(x)
        x = self.bn1(x)
        x = self.act1(x)
        x = self.blocks(x)
        x = self.global_pool(x)
        x = self.conv_head(x)
        x = self.act2(x)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.flatten(x)
        if self.dropout > 0.:
            x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.classifier(x)
        return x


def _create_ghostnet(variant, width=1.0, pretrained=False, **kwargs):
    """
    Constructs a GhostNet model
    """
    cfgs = [
        # k, t, c, SE, s 
        # stage1
        [[3,  16,  16, 0, 1]],
        # stage2
        [[3,  48,  24, 0, 2]],
        [[3,  72,  24, 0, 1]],
        # stage3
        [[5,  72,  40, 0.25, 2]],
        [[5, 120,  40, 0.25, 1]],
        # stage4
        [[3, 240,  80, 0, 2]],
        [[3, 200,  80, 0, 1],
         [3, 184,  80, 0, 1],
         [3, 184,  80, 0, 1],
         [3, 480, 112, 0.25, 1],
         [3, 672, 112, 0.25, 1]
        ],
        # stage5
        [[5, 672, 160, 0.25, 2]],
        [[5, 960, 160, 0, 1],
         [5, 960, 160, 0.25, 1],
         [5, 960, 160, 0, 1],
         [5, 960, 160, 0.25, 1]
        ]
    ]
    model_kwargs = dict(
        cfgs=cfgs,
        width=width,
        **kwargs,
    )
    return build_model_with_cfg(
        GhostNet, variant, pretrained,
        default_cfg=default_cfgs[variant],
        feature_cfg=dict(flatten_sequential=True),
        **model_kwargs)


@register_model
def ghostnet_050(pretrained=False, **kwargs):
    """ GhostNet-0.5x """
    model = _create_ghostnet('ghostnet_050', width=0.5, pretrained=pretrained, **kwargs)
    return model


@register_model
def ghostnet_100(pretrained=False, **kwargs):
    """ GhostNet-1.0x """
    model = _create_ghostnet('ghostnet_100', width=1.0, pretrained=pretrained, **kwargs)
    return model


@register_model
def ghostnet_130(pretrained=False, **kwargs):
    """ GhostNet-1.3x """
    model = _create_ghostnet('ghostnet_130', width=1.3, pretrained=pretrained, **kwargs)
    return model
